Llama 4 Scout
Meta's efficiency model. Long-context specialist with 10M window.
Scores by test
Methodology →What you need to know
Llama 4 Scout is primarily a long-context utility model, distinguished by a perfect 5/5 internal score for long-context handling and a substantial 328K context window. It excels at processing large datasets and maintaining faithfulness, making it effective for retrieval-heavy tasks where accuracy and volume are prioritized over reasoning depth.
The model is highly efficient for structured operational tasks, scoring 4/5 in tool calling, classification, and structured output. These strengths, combined with a low blended cost of $0.245/MTok, make it an economical choice for high-volume pipelines that require strict formatting or multilingual support.
However, the model lacks advanced cognitive capabilities. With scores of 2/5 in strategic analysis and agentic planning, it is unsuitable for autonomous decision-making or complex multi-step reasoning. Its low safety calibration score also suggests a need for rigorous external guardrails in production environments.
Use this model if you need a low-cost solution for analyzing very large documents, performing classification, or extracting structured data across multiple languages. Skip this model if your application requires complex planning, strategic reasoning, or high-precision safety alignment.
Strengths — Top 3
Relative weaknesses — Bottom 3
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